PixelPrune: Pixel-Level Adaptive Visual Token Reduction via Predictive Coding

TL;DR

PixelPrune is a training-free, pixel-level redundancy reduction method that accelerates Vision-Language Model inference by pruning duplicate image patches before the transformer encoder, maintaining accuracy.

Contribution

It introduces a novel pixel-level redundancy exploitation technique using predictive coding, enabling fast, lossless or lossy image patch pruning prior to neural processing.

Findings

Achieves up to 4.2× inference speedup

Provides 1.9× training acceleration

Maintains competitive accuracy across benchmarks

Abstract

Document understanding and GUI interaction are among the highest-value applications of Vision-Language Models (VLMs), yet they impose exceptionally heavy computational burden: fine-grained text and small UI elements demand high-resolution inputs that produce tens of thousands of visual tokens. We observe that this cost is largely wasteful -- across document and GUI benchmarks, only 22--71\% of image patches are pixel-unique, the rest being exact duplicates of another patch in the same image. We propose \textbf{PixelPrune}, which exploits this pixel-level redundancy through predictive-coding-based compression, pruning redundant patches \emph{before} the Vision Transformer (ViT) encoder. Because it operates in pixel space prior to any neural computation, PixelPrune accelerates both the ViT encoder and the downstream LLM, covering the full inference pipeline. The method is training-free, requires no learnable parameters, and supports pixel-lossless compression ($\tau{=}0$) as well as controlled lossy compression ($\tau{>}0$). Experiments across three model scales and document and GUI benchmarks show that PixelPrune maintains competitive task accuracy while delivering up to 4.2$\times$ inference speedup and 1.9$\times$ training acceleration. Code is available at https://github.com/OPPO-Mente-Lab/PixelPrune.